Abstract
BackgroundOver the past several decades researchers have produced substantial evidence of a social gradient in a variety of health outcomes, rising from systematic differences in income, education, employment conditions, and family dynamics within the population. Social gradients in health are measured using deprivation indices, which are typically constructed from aggregated socio-economic data taken from the national census – a technique which dates back at least until the early 1970's. The primary method of index construction over the last decade has been a Principal Component Analysis. Seldom are the indices constructed from survey-based data sources due to the inherent difficulty in validating the subjectivity of the response scores. We argue that this very subjectivity can uncover spatial distributions of local health outcomes. Moreover, indication of neighbourhood socio-economic status may go underrepresented when weighted without expert opinion. In this paper we propose the use of geographic information science (GIS) for constructing the index. We employ a GIS-based Order Weighted Average (OWA) Multicriteria Analysis (MCA) as a technique to validate deprivation indices that are constructed using more qualitative data sources. Both OWA and traditional MCA are well known and used methodologies in spatial analysis but have had little application in social epidemiology.ResultsA survey of British Columbia's Medical Health Officers (MHOs) was used to populate the MCA-based index. Seven variables were selected and weighted based on the survey results. OWA variable weights assign both local and global weights to the index variables using a sliding scale, producing a range of variable scenarios. The local weights also provide leverage for controlling the level of uncertainty in the MHO response scores. This is distinct from traditional deprivation indices in that the weighting is simultaneously dictated by the original respondent scores and the value of the variables in the dataset.ConclusionOWA-based MCA is a sensitive instrument that permits incorporation of expert opinion in quantifying socio-economic gradients in health status. OWA applies both subjective and objective weights to the index variables, thus providing a more rational means of incorporating survey results into spatial analysis.
Highlights
Over the past several decades researchers have produced substantial evidence of a social gradient in a variety of health outcomes, rising from systematic differences in income, education, employment conditions, and family dynamics within the population
Our analysis uses an Order Weighted Average (OWA) weighting algorithm, which was selected based on its ability to represent both the original and a data-driven ranking of the variables selected by the Medical Health Officers (MHOs)
The utility of Multicriteria Analysis (MCA) is in synthesizing the variables and the weights assigned by the MHOs into a single, quantifiable model reflective of the socio-economic variables chosen by the experts, their weight, and the rank importance of their value existent in the dataset
Summary
Over the past several decades researchers have produced substantial evidence of a social gradient in a variety of health outcomes, rising from systematic differences in income, education, employment conditions, and family dynamics within the population. We employ a GIS-based Order Weighted Average (OWA) Multicriteria Analysis (MCA) as a technique to validate deprivation indices that are constructed using more qualitative data sources. Deprivation indices are most frequently constructed using variations of either Principal Component or Factor Analysis – both of which are highly computational structural detection and data reduction strategies designed to reduce the number of variables needed to measure socio-economic status (SES). These strategies minimize the opportunity to incorporate the knowledge of local health practitioners and of the day-to-day conditions that impact local health outcomes. Our analysis uses an Order Weighted Average (OWA) weighting algorithm, which was selected based on its ability to represent both the original and a data-driven ranking of the variables selected by the MHOs
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